# app.py - FastAPI Service for Floral Art RAG System
import os
import base64
import json
import logging
import requests
from typing import Dict, Any, List, Optional
from fastapi import FastAPI, HTTPException, BackgroundTasks, Form, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from dotenv import load_dotenv

# Import our RAG system
from floral_rag import FloralArtApp, FloralRAGSystem

# Load environment variables
load_dotenv()
DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
TTS_API_KEY = os.getenv("TTS_API_KEY", "")  # For speech synthesis
STT_API_KEY = os.getenv("STT_API_KEY", "")  # For speech recognition
TEXT_TO_IMAGE_API_KEY = os.getenv("TEXT_TO_IMAGE_API_KEY", "")  # For text-to-image

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Initialize the Floral Art application
floral_app = FloralArtApp()

# FastAPI app
app = FastAPI(
    title="Floral Art AI API",
    description="API for floral arrangement knowledge and design using RAG and DeepSeek API",
    version="1.0.0"
)

# Enable CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify actual origins
    allow_methods=["*"],
    allow_headers=["*"],
)

# Request and response models
class QueryRequest(BaseModel):
    query: str
    generate_image: bool = False
    
class UploadRequest(BaseModel):
    file_path: str
    file_name: str

class QueryResponse(BaseModel):
    response: str
    source: str  # "local" or "rag+api"
    image_url: Optional[str] = None

class ImageGenerationRequest(BaseModel):
    prompt: str
    style: str = "realistic"  # Default style
    
class AudioTranscriptRequest(BaseModel):
    audio_base64: str
    
# Routes
@app.get("/")
async def root():
    """Root endpoint"""
    return {"message": "Floral Art AI API is running"}

@app.post("/query", response_model=QueryResponse)
async def process_query(request: QueryRequest, background_tasks: BackgroundTasks):
    """Process a text query about floral art"""
    try:
        # Process the query
        response, source = floral_app.rag_system.process_query(request.query)
        
        result = {"response": response, "source": source}
        
        # If image generation requested, generate image in the background
        if request.generate_image:
            # Extract prompt for image generation from the response
            image_prompt = f"floral arrangement, {request.query}"
            
            # In actual implementation, call your text-to-image API here
            # For now, we'll return a placeholder URL
            result["image_url"] = "https://placeholder.com/floral_image.jpg"
            
            # Schedule image generation in background
            # background_tasks.add_task(generate_image, image_prompt)
            
        return result
    
    except Exception as e:
        logger.error(f"Error processing query: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")

@app.post("/upload-knowledge")
async def upload_knowledge(file: UploadFile):
    """Upload a document to the knowledge base"""
    try:
        # Save the uploaded file
        file_path = f"temp/{file.filename}"
        os.makedirs("temp", exist_ok=True)
        
        with open(file_path, "wb") as f:
            content = await file.read()
            f.write(content)
        
        # Add to knowledge base
        floral_app.setup_knowledge_base([file_path])
        
        # Clean up temp file
        os.remove(file_path)
        
        return {"message": f"Successfully added {file.filename} to knowledge base"}
    
    except Exception as e:
        logger.error(f"Error uploading file: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error uploading file: {str(e)}")

@app.post("/transcribe-audio")
async def transcribe_audio(request: AudioTranscriptRequest):
    """Transcribe audio to text"""
    try:
        # Decode base64 audio
        audio_data = base64.b64decode(request.audio_base64)
        
        # Save to temporary file
        temp_file = "temp/audio.wav"
        os.makedirs("temp", exist_ok=True)
        
        with open(temp_file, "wb") as f:
            f.write(audio_data)
        
        # In a real implementation, call an STT API
        # For now, return a placeholder
        transcription = "这是一个关于插花艺术的语音转文字示例。"
        
        # Clean up
        os.remove(temp_file)
        
        return {"transcription": transcription}
    
    except Exception as e:
        logger.error(f"Error transcribing audio: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error transcribing audio: {str(e)}")

@app.post("/generate-image")
async def generate_floral_image(request: ImageGenerationRequest):
    """Generate an image of a floral arrangement"""
    try:
        # In a real implementation, call a text-to-image API
        # For now, return a placeholder
        
        # Enhance the prompt for better floral images
        enhanced_prompt = f"A professional floral arrangement with {request.prompt}, {request.style} style, detailed, studio lighting, professional photography"
        
        # Placeholder response
        return {
            "image_url": "https://placeholder.com/floral_image.jpg",
            "prompt": enhanced_prompt
        }
    
    except Exception as e:
        logger.error(f"Error generating image: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error generating image: {str(e)}")

@app.post("/train-local-model")
async def train_model(background_tasks: BackgroundTasks):
    """Start training the local model (async)"""
    try:
        # Start training in background
        background_tasks.add_task(floral_app.train_local_model)
        return {"message": "Local model training started in background"}
    
    except Exception as e:
        logger.error(f"Error starting model training: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error starting model training: {str(e)}")

@app.get("/system-status")
async def system_status():
    """Get system status"""
    return {
        "knowledge_base": {
            "document_count": 50,  # Placeholder - in real app, count documents
            "last_updated": "2023-04-11T12:00:00Z"
        },
        "local_model": {
            "available": floral_app.rag_system.local_model.is_available(),
            "model_path": floral_app.rag_system.local_model.model_path
        },
        "api_status": "active",
    }

# Helper functions
async def generate_image(prompt: str):
    """Background task to generate an image"""
    logger.info(f"Generating image for prompt: {prompt}")
    # Implementation would call your text-to-image API

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
